Method and system for the prediction, rapid detection, warning, prevention, or control of changes in the brain states of a subject using hurst parameter estimation

Abstract

A system for analyzing signals representative of a subject's brain activity in a signal processor for information indicating the subject's current activity state and for detecting or predicting a change in the activity state. One preferred embodiment uses a method for estimating the Hurst parameter to perform real-time analysis of the electroencephalogram (EEG) or electrocorticogram (ECoG) signals from a subject patient for information indicative of or predictive of a seizure, and to complete the needed analysis at least before clinical seizure onset. The preferred system then performs an output task for prevention or abatement of the seizure, or for recording pertinent data.

1. A method for detecting and quantifying an epileptic seizure in a subject, comprising the steps of:
 (a) receiving signals from a plurality of sensors indicative of brain state of a subject into a processor; 
 (b) using the processor to estimate at least one Hurst parameter of the signals in moving time windows and a spatio-temporal propagation of the estimate of the at least one Hurst parameter of the signals in the moving time windows; 
 (c) detecting an epileptic seizure by determining if a change in the spatio-temporal propagation of the estimate of the at least one Hurst parameter estimate is indicative of an epileptic seizure in the subject; and 
 (d) determining if at least one feature of the detected epileptic seizure quantifies the seizure, wherein said feature is selected from a set consisting of duration, intensity, onset location, degree of spread, propagation path and speed through regions of the brain being monitored by the plurality of sensors. 
 
     
     
       2. The method of  claim 1 , wherein the at least one Hurst parameter is estimated using one of a set consisting of a rescaled range statistic, dispersional analysis, bridge de-trended scaled window variance, correlogram method, the use of partial correlations, variance plots, variogram, least squares progression in the spectral domain, Higushi's method, Peng's residuals of regression method, Kettani and Gubner's method of direct estimation from the autocorrelation function, and Abry and Veitch's wavelet-based method. 
     
     
       3. The method of  claim 1 , wherein the Hurst parameter is estimated using a broadband signal spanning a range of approximately 0 to 2000 kHz. 
     
     
       4. The method of  claim 1 , wherein the Hurst parameter is estimated using a signal sampled with a sampling frequency of at least 200 Hz. 
     
     
       5. The method of  claim 1 , wherein the Hurst parameter is estimated using a signal sampled with a sampling rate below 200 Hz. 
     
     
       6. The method of  claim 1 , wherein the Hurst parameter is estimated from a narrow-band signal spanning a frequency range from approximately 0.5 Hz to 70 Hz. 
     
     
       7. The method of  claim 1 , wherein the Hurst parameter is estimated from an unfiltered signal. 
     
     
       8. The method of  claim 1 , wherein the Hurst parameter is estimated from a preprocessed signal. 
     
     
       9. The method of  claim 1 , including an additional step of triggering a warning, delivering a therapy, logging an event or storing information or data. 
     
     
       10. A method as described in  claim 1 , further comprising the step of:
 (e) outputting a result of the detecting of an epileptic seizure to at least one device for warning, therapeutic intervention, monitoring, or data storage. 
 
     
     
       11. A system for detecting and quantifying an epileptic seizure in a subject, comprising:
 (a) receiving means configured to receive signals from a plurality of sensors, wherein the signals are indicative of a brain state of a subject; 
 (b) a processor configured to:
 (1) estimate at least one Hurst parameter of the signals in moving time windows, 
 (2) determine a spatio-temporal propagation characteristic of the at least one Hurst parameter estimate, 
 (3) detect an epileptic seizure by determining if a change in the spatio-temporal propagation of the at least one Hurst parameter estimate is indicative of an epileptic seizure in the subject, and 
 (4) determine at least one feature of the detected epileptic seizure that quantifies the seizure, wherein said feature is selected from a set consisting of duration, intensity, onset location, degree of spread, propagation path and speed through regions of the brain of the subject being monitored by the plurality of sensors; and 
 
 (c) output means configured to produce an output indicative of the occurrence of an epileptic seizure of the subject.